When valuable media items such as bank notes are fed into an automatic handling system, such as banknote sorter or an intelligent deposit or recycling ATM, the system must recognize the items to determine to which of multiple possible classes (e.g., which currency and denomination) each item belongs and, on recognition, must assess whether the item is genuine or counterfeit (i.e., “validate” the item). These systems typically achieve recognition and validation of valuable items by using templates developed previously from training data taken from items of the type to be handled. In practice, a unique template is developed for every unique “class” of media item that the systems are expected to handle. For example, for currency-handling systems, a unique template is created for every possible combination of currency, series, denomination and orientation (i.e., orientation of the bank note upon insertion into the handling system).
Developing a unique template for each class of media item becomes very time consuming and very costly, especially when developing currency handlers for countries having bank notes produced in many series or issued by multiple banks or treasuries. Because most existing validation methods require manual selection of the relevant features (regions) of bank notes by a human expert, the costs of template creation become even more pronounced. In some cases, a new template must be developed even for changes as minor as a new official's signature appearing on a bank note or a slight change in the ink used to print the note.